Machine Learning Engineer, Applied AI

Role overview

Develop and operate machine learning systems that improve classification, search, ranking, retrieval, and personalization in a consumer application. The work spans model development, evaluation, deployment, and ongoing production monitoring, with attention to relevance, quality, speed, and inference cost.

Responsibilities

- Develop and refine models for classification, retrieval, ranking, and personalization. - Build classifiers and ranking systems, balancing predictive quality with latency and inference expense. - Improve search and context retrieval using embeddings, hybrid search, and reranking approaches. - Create personalization models using behavioral and product data. - Build datasets, evaluation pipelines, and metrics to assess quality and identify regressions. - Train, fine-tune, and deploy models through production ML workflows. - Monitor deployed models, investigate issues, and use evaluations and user feedback to guide improvements.

Requirements

- At least three years of machine learning engineering experience, including production deployments. - Strong Python skills and experience with PyTorch or JAX. - Knowledge of machine learning methods, model training, and evaluation. - Experience with classification, ranking, retrieval, recommendation, or personalization models. - Experience with embeddings, vector search, or information retrieval. - Ability to build data pipelines, training workflows, and model inference services. - Skill in evaluating models with relevant metrics and experiments. - Understanding of trade-offs among accuracy, latency, compute cost, and reliability.

Nice to have

- Experience with transformer models, language model fine-tuning, or embedding models. - Familiarity with hybrid search, reranking, and retrieval evaluation. - Experience with recommendation or personalization for consumer products. - Background in large-scale ML training or inference infrastructure.